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|
Figures 1-5
Return to top.
The automatic tracking of seismic horizons has been widely available in
commercial software since the early 1990s providing our first insight
into the problem of interpretation automation for geologic faults. What
is immediately obvious with a horizon auto-tracker is that the tracking
frequently breaks down at fault boundaries. Depending on the tracker,
and the parameter settings, we observe gaps in the resulting interpreted
surface (non-picked areas) and possibly large time jumps where the
auto-tracker picks an erroneous event. Consider the example where the
horizon we are tracking encounters a fault that has a displacement equal
(in time) to some multiple of our dominate seismic frequency (Figure
1). In this case, our algorithm cannot distinguish an unfortunate
alignment of seismic character across the fault without additional
information to “recognize” that we have encountered a faulted surface.
Using a larger window, encompassing more of the wave train could
potentially capture the offset on neighboring events. Or a more
sophisticated approach could use simultaneous tracking of multiple
horizons, reducing the likelihood for misalignment.
Most automatic horizon tracking applications include cross-correlation
or waveform based tracking algorithms to capture the seismic character
over a user controlled window length. These methods also compute a
“quality factor” attribute associated with the horizon pick position,
which give us a further indication on areas of faulting. The combination
of interpretation gaps, large gradient trends, and connected regions of
low quality factor can produce an excellent visual isolation of the
fault geometry, relative to the background horizon structure.
While the fault expression was made visible from the horizon
auto-tracking method alone, as shown in Figure 2,
the means to extract this fault information directly and automatically
was not available. A clever approach to isolate the fault information
from an auto-picked horizon is to take the inverse of the surface, i.e.
show only areas where the interpretation does not exist.
Figure 3 shows an example of the inverse
operation on a surface. The fault boundaries for the structural extent
of the horizon are clearly visible. This technique must be applied to
each surface and then linked from between one surface to the next, if a
complete fault surface is required. Not really an automatic process, but
it does allow an un-bias extraction of faults from a statistically
consistent auto-tracker. Surface operations can be a powerful tool set
for deriving additional information from surfaces and surface
properties. Workflow or process managers and object calculators are
technologies not yet fully exploited by geoscientists.
An early effort for semi-automatic fault interpretation came from
Landmark Graphics Corporation when they introduced FZAP! technology in
1997 (Hutchinson, Simpson et al., US Patent Number 5,537,320). This
technique allowed users to begin their fault interpretation task by
simply “seeding” one or more fault segments (sticks) on a vertical
seismic section, and the automatic operation would perform a
cross-correlation on a series of slanted traces derived parallel to the
seeded fault segment. The method could be used for both tracking, where
no previous fault interpretation existed, or snapping, where an existing
fault interpretation would be corrected based on the slant trace
cross-correlation algorithm. Each fault surface extracted would need an
initial seed point.
A “seedless” approach to fault segment extraction was presented by van
Bemmel and Pepper (1999, US Patent Number 5,999,885), where the gaps and
sharp gradients from a horizon interpretation are subjected to a
connected body analysis followed by feature testing to deduce likely
fault candidates. Through the analysis of multiple horizons, the entire
fault framework could be extracted.
Seismic signal process advanced rapidly during the 1990s, allowing us to
approach the problem of fault interpretation automation in a similar
vein as we attack horizon interpretation. Bahorich and Farmer (1995)
present The Coherency Cube (US Patent Number 5,563,949), a seismic
attribute for imaging discontinuities. They note that fault surfaces are
distinctly separated from neighbouring data, both visually and
numerically, enabling auto-picking with the existing horizon
auto-tracking software. Lees (1999) directly demonstrates this
methodology using a voxel-picking algorithm on a seismic cube processed
with a semblance attribute. Crawford and Medwedeff (1999, US Patent
Number 5,987388) demonstrate extracting faults from the 3D seismic cube
by performing linear feature detection on lateral slices through the
seismic discontinuity volume. The BP Center for Visualization at the
University of Colorado continues to further develop this work, and it is
commercially available through Paradigm. These methods all help us
recognize that the fault expression in the seismic, after discontinuity
processing, is most visible in the time-slice or horizon-slice
orientations. Neff et al. (2000, US Patent Number 6,018,498) introduce a
method that uniquely combine many of these elements by estimating a
probability factor that a fault exists at a specific spatial location
using parallel estimation planes within the seismic volume, and then
following this procedure with an orientation and extraction method based
on linear feature detection on time slices.
These new edge attributes teach us that a vertical seismic section may
not be the best background canvas for fault interpretation. By
visualizing seismic discontinuity volumes as time slices, the major
seismic interpretation systems are well suited for fault interpretation,
as seen in Figure 4. Seismic attribute
processing highlight the spatial extent of each fault , allowing accurate
manual fault picking on these time-slice images. By connecting the line
interpretation on just a few time-slices, a high quality fault surface
can be constructed.
The small additional step of executing seeded fault auto-picking on
these edge volumes is just entering the mainstream in terms of a
commercial software offering. The reason for this technology delay may
be in our historical approach of using the seismic interpretation
workstation to emulate our “paper” interpretation from yesteryear. We
characteristically use the seismic workstations to pick “ fault sticks”
on vertical seismic sections and then link the intersection of these
fault sticks with the interpreted seismic horizon to develop fault
traces, in basically the same technique used historically for a
paper-based interpretation. Fault contacts are transferred from their
position in the vertical seismic section to their spatial position on a
basemap for contouring of the seismic horizon. In this sense, the faults
are disposable since we are really just interested in fault planes
intrusion into the horizon map (surface inverse). Our seismic
interpretation workstations simply emulate our manual interpretation
process; see Figure 5. We manually draw our
fault sticks on the seismic section, establish the fault contact points,
and then see them posted on the basemap.
The current generation of geological modelling packages treat fault
surfaces as legitimate objects in a 3D structural framework, and further
the cause of introducing more un-biased and automatic methods for the
identification and extraction of fault surfaces. Technologies for 3D
rendering, fast computation, and maturing signal processing workflows
may finally move us away from our “paper” interpretation mindset. Let’s
now examine some key contributors towards the advancement of fault
interpretation automation.
Figures 6-9
Many emerging technologies contribute to our understanding of subsurface
faulting and fracturing. We recognize that much progress has been made
in the use of the shear-wave component for fracture identification, but
that’s a different story. For now, we shall focus on reviewing a
collection of enabling technologies, which highlight the advances toward
the interpretation automation of seismically resolvable faults and
fractures. Our working definition of “seismically resolvable faults are
fractures” means those features that express themselves through a
spatially coherent measure derived from a typical 3D compressional-wave
seismic survey. This measure could mean either a measure of
discontinuity or another seismic attribute that allows cognitive
identification and isolation of the fault feature.
We hope that by this point you can accept that discontinuity processing
of seismic data, via signal processing of the entire cube, or as a
by-product of horizon auto-tracking, enable us to visually isolate fault
features in the seismic data, particularly in a horizontal format
(either surface slices or time slices). This acceptance opens the door
that interpretation automation may be possible, but issues remain. Can
we improve the quality of our images sufficiently for algorithmic
extraction of the fault features? Our images contain a significant
amount of noise, or acquisition/processing artifacts that reduce their
quality for automated threshold type picking or extraction. A simple
example can demonstrate this point; consider the seismic horizon in
Figure 6a, for a horizon has been
auto-tracked. The tracking algorithm constantly encounters
discontinuities in the data that lead to cycle-skips, or holes in the
interpreted result unrelated to fault breaks in the data. For structural
tracking, we could consider a signal processing step of smoothing our
input seismic data first, thus allowing the auto-tracker a must more
consistent signal to follow, Figure 6b. This
example also demonstrates using other seismic attributes as possible
input volumes for surface tracking, i.e. an apparent polarity section.
Fast volumetric signal processing is becoming a basic element of the
geoscientist’s toolkit, as evident in the barrage of technical papers
and patents related to advanced signal processing on post-stack seismic
volumes. A good example of incorporating signal processing and seismic
interpretation are a pair of papers by Fehmers and Hocker (2002, 2003)
on fast structural interpretation with structure-oriented filtering.
They making a convincing argument that data conditioning before
automatic interpretation produces more complete areal coverage and
improved picking stability. Further, they describe their method to
reduce noise without degradation to the fault expression contained in
the original data. Randen et.al. (2000) demonstrated a collection of
seismic attributes that can be derived from local structural orientation
estimates to further advance automated interpretation.
Figure 7 shows the effectiveness of
smoothing along the local structural estimate (7c)
versus smoothing that does not honor structure (7b).
Marfurt et al. (1999) further develop seismic discontinuity processing
in the presence of local structure using a smoothed local estimate. Chen
et al. (2003) offer an alternative method for imaging discontinuities
using dip-steering. Both are examples of processes, which benefit from a
priori knowledge of the local structure. Sudhakar et al. (2000)
familiarize us with the advantage of incorporating azimuthal variation
into our methodology for detecting faults and fractures. They
demonstrate the superior results obtainable by using restrictive
azimuthal volumes during processing and attribute generation. Most
commercial seismic attribute packages today offer some version of a
seismic dip and seismic azimuth attributes or attributes that derive
local structure during calculation.
Many new signal-processing methods are being developed and entering
commercial packages, exploiting properties of local curvature (Roberts,
2001), local frequency variability (Partyka et al., 1999), and seismic
textures (Randen and Iske, 2005) for example. With this vast array of
seismic attribute volumes, classification and neural network analysis
are natural solutions for extraction or isolation of seismic objects.
Identification of faults by combining multi-attribute analysis with
neural network classification is another maturing area. Meldahl et.al.
(2001) remark that the trend is shifting from horizon-based towards
volume-based interpretation. We are replacing surface and fault drawing
with seismicobject detection methods, combining fit-for-purpose
attribute processing with pattern recognition technologies. Others
continue to exploit the horizon-based methods, but adopt a more global
approach by simultaneously operating on a collection of derived
surfaces. Alberts et.al., (2000) demonstrate a neural net method for
multi horizon tracking across discontinuities. This method is attractive
because it allows multiple input volumes (i.e. seismic attributes) to be
directly incorporated in the training and the estimation. As the authors
point out, classifying and tracking several horizons at the same time
provide additional constraints and enable better performance of the
neural network during learning. They recognize that this method has a
problem with lateral changes in the character of the horizons, but
suggest that dynamic retraining may offer a solution.
A more sophisticated collection of attributes were used by Borgos et.al.
(2003) to isolate and capture the significant characteristic of the
seismic events at extrema positions only. Using a trace decomposition, a
reflector can be represented with one-point support. The output is a
spare cube with class values only at the minimum or maximum positions of
the original input seismic data. Notice the consistent vertical sequence
of classes across the fault boundaries in Figure
8.
Borgos, et.al., (2003) take the analysis further by including a fault
displacement estimation by extrapolation of the classification results
onto existing fault surfaces, and calculating the displacement as a
distance along the fault surface to extrema class pairs from either side
of the fault . The fault surface now contains an additional spatially
variable property of displacement. Skov et.al., (2004) demonstrate the
use of the fault displacement property as a component of fault system
analysis. Admasu and Toennies (2004) produce a fault displacement model
by performing discreet matching of prominent regions across fault
planes. Aurnhammer and Tönnies introduce a genetic algorithm for
non-rigid matching across faults.
These examples suggest another important element in our quest. The
integrated interpretation of faults and horizons, through iterative
interpretation or simultaneous interpretation will help us converge on a
more accurate structural framework. Tingdahl et al. (2002) offer one
example of mapping faults and horizons concurrently, extending the work
of Statoil’s seismic object detection technology (Meldahl et al., 2001).
S.I. Pedersen et.al. ( 2002, 2003) introduced a method known as
ant-tracking, based on artificial swarm intelligence. This is an
exciting method where many thousands of computational “agents” are
deployed in a volume to extract a small patch of the discontinuity. The
redundancy of agents over the same area reinforces and extends the
extracted feature while increasing the confidence in estimate.
Figure 9 shows the result of running
ant-tracking on an edge volume to create both an enhanced edge volume
and to automatically extract fault patches.
Another method offered by Goff et.al. (2003, US Patent Application
20030112704) extracts a fault network skeleton by utilizing a minimum
path value and further subdividing a network into individual fault
patches wherein the individual patches are the smallest,
non-intersecting, nonbifurcating patches that lie on only one geologic
fault . This introduction of a patch concept is exciting because it also
introduces the idea of patch properties. We now have an additional means
of segmenting our fault information.
Figure 10-14
![](thumbs/10.jpg) |
Figure 10. Ant Cube viewed as time slice
to guide fault interpretation, while the vertical section is the
structural smoothed seismic interpretation of horizons. Horizon
can be auto-picked initially from smoothed seismic for regional
extend, then snapped to original seismic for amplitude
extraction. The faults can be auto-picked from the edge volume,
manually interpreted (red) on the time slices, or automatically
extracted from the data as surfaces (Figure
9). |
![](thumbs/11.jpg) |
Figure 11. Fault interpretation
workflows include pre-conditioning, edge detection, edge
enhancement, pos-tconditioning, followed by fault extraction via
automatic methods and structural filtering, seeded
auto-tracking, or manual interpretation. |
![](thumbs/12.jpg) |
Figure 12. Histogram and Stereonet
filtering of fault patch collections allow fault system level
interpretation. Patches have the advantage of containing
properties (average azimuth, average dip, size, confidence…),
which could be extended for manually interpreted faults as well. |
![](thumbs/13.jpg) |
Figure 13. Voxel information extracted
from structural smoothing, frequency filtering, discontinuity
processing (Variance), followed by fault enhancement
(Ant-tracking). The fault enhanced seismic volume is re-sampled
to a 3D property grid. Within the geologic modeling process,
properties, such as permeability, can be assigned to the fault
expression based on a threshold value. |
![](thumbs/14.jpg) |
Figure 14. A displacement attribute can
be constructed by utilizing the variation in local structure in
the continuous areas in combination with fault throw estimates.
Trace-to-trace coherence can be used as a guide for where
automation will be likely to breakdown. (Spatially distributed
offsets image from Skov et al., 2004). |
Interpretation automation differs conceptually from automated
interpretation. The goal of the first is to provide a tool to improve
the quality and turn-around time for interpretation, whereas the latter
implies a promise of providing an interpretation without human
intervention. While a few corporate executives may like the idea of
“click here to find oil”, the geoscientist needs a flexible software
toolset which can automate where appropriate, supplemented with manual
input when necessary, and most importantly offer a means of extracting
the desired information easily.
This desired fault information can be classified in two different forms,
implicit or explicit. An explicit representation means surfaces are
created and can then be used for framework and geologic model
construction. The simplest case here would be a traditional map of an
interpreted horizon, showing the intersection with the fault surfaces
and bounded gaps in the horizon surface, as previously shown in
Figure 3. True 3D geologic modeling requires
the additional step of fault surface intersection interpretation to the
bound layers.
Looking at the explicit method in more detail, we can summarize an
approach to leverage the enabling technologies previously discussed. We
would like to move away from a basemap representation of our prospect to
a true 3D model representation. One limitation in the past has been the
difficulty to performing traditional interpretation, i.e. horizon and
fault drawing, in a 3D canvas with the same ease they are currently
performed in a 2D canvas. When emulating paper interpretation, a 2D view
with polyline drawing functional is appropriate. If the interpretation
paradigm changes from manual drawing to surface or volume extraction,
the 3D canvas becomes the premier choice. An efficient presentation
style for joint horizon/ fault interpretation would be to show vertical
plane through the seismic amplitude cube and a timeslice view of the
discontinuity cube; see Figure 10.
For automatic extraction techniques, the seismic data must be
pre-conditioned either during the extraction process or as a preliminary
processing step. In addition, there may be multiple versions of the
seismic data or derived attributes required depending on the
interpretation objective. For example, regional structure and major
fault interpretation can be performed on structurally smoothed data with
great benefit, but at the expense of small fault displacement expression
and a loss of subtle amplitude variations. Yet, once this regional
framework is in place, we can return to our original data, pre-condition
the data to emphasis the small features and interpret them in their best
light.
For fault extraction, the construction of a discontinuity volume allows
the direct detection of seismic faults. We again have the option to
further condition the discontinuity data to emphasis large-scale
features and/or the subtle detail. Digital processing libraries that
offer directional filtering, connectivity filtering, volume
segmentation, morphology operations, and multi-volume operations can all
be utilized to further visually isolate our features of interest.
Post-processing of the discontinuity volume can further isolate the
interesting features. Processes such as skeletonizing, pruning,
thinning, and erosion (Gonzalez and Woods, 1992) can be powerful
filters. Other possibilities are iterative operations, such as running
Ant-tracking on the results of Ant-tracking.
While the commercial market has a wonderful inventory of signal
processing methods for seismic volumes, the tools for surface extraction
from seismic volumes has been lacking. Seeded autotracking for faults is
not yet mainstream, but we can anticipate they will soon be widely
available. In addition, more sophisticated approaches for global
extraction of fault surfaces; e.g., AntTracking and neural net
classification methods, are also entering the marketplace and will
continue to mature. Parallel to these developments, hardware with enough
processing power to compute multi-trace attributes for larger seismic
volumes and the corresponding disk space to persist those results have
become more affordable to users in general. If this trend continues,
then a carefully designed software platform that can host these
workflows and can provide a simple interface to control the different
steps, will surely contribute to make these newer techniques more
attractive. See Figure 11 for fault
interpretation workflow.
These advances open the door for the geoscientist to work with the
derived fault information in more meaningful ways. One of the greatest
advantages of the migration from paper interpretation to the workstation
was the opportunity to easily access the amplitude information from the
seismic. This advantage can now be extended to faults. As previously
mentioned, extracted fault patches can be filtered based on their
properties (size, quality, orientation, average throw…) but this concept
can also be extended to all fault objects regardless of the method used
to extract them. Automatic and manual fault interpretation can be
managed on a fault system level by filtering on one or more of the
derived properties associated with the collection. New properties can be
added to estimate fault connectivity, strike length, etc., which will be
useful in support of well-based fracture network density analysis.
Schlumberger Stavanger Research developed and presented interpretation
workflows based on system level interpretation of faults by utilizing
these collection of properties associated with extracted fault patches
as visual filters, S.I. Pedersen et.al. ( 2002), Borgos, et.al. (2003),
and Skov et.al. (2004). Simple histogram and orientation filtering allow
the interpreter to reduce an automatically derived collection of fault
patches into meaningful fault systems (Figure 12).
The second form of extracting the fault information is an implicit
representation, where the seismic is re-sampled into the geologic model
as the container for the fault knowledge. A simple example here would be
to take the fault expression from discontinuity processing (or further
enhancement processing of faults), then re-sample this voxel information
into the 3D property grid model (Figure 13).
Incorporating implicit fault definitions with seismically constrained
layer property population will yield high-resolution geologic models.
Obviously, a voxel representation of a fault could be converted to an
explicit surface representation through surface modeling options, i.e.,
gridding. Implicit methods can be made more sophisticated through
advanced signal processing and custom workflows. It is not a great leap
to appreciate that the seismic displacement field itself would be a
valuable seismic attribute.
The 3D displacement field means that at any x,y,z location, we could
determine the geologically equivalent position at all other locations in
the prospect area. A novel means of constructing an implicit geologic
model would be to stochastically populate a model at log resolution, but
structurally guide the statistics along coherent orientation and across
fault breaks from the displacement estimate. The displacement field
would also be a welcome addition to volume restoration studies in
support of structural geology interpretation. Dee et.al. (2005),
acknowledge fault correlation from seismic as having immediate impact on
structural geologic analysis best practices, but their perspective is
from primarily manual interpretation methods, and does not include the
orientation estimate available from seismic and the automation
processes.
An automated means of producing this displacement field would require
the combination of two separate elements. We could determine the
displacement of a continuous seismic event by computing the local
orientation of the horizon. With the dip and azimuth computation at a
point, we could predict where the event will on the neighboring traces.
But this only will work for continuous events. When we encounter a
fault , the orientation estimate will not give us the fault throw, and in
fact we will not get a reliable orientation estimate in the vicinity of
a fault . Here we must introduce the second element of our automation
approach, which is to compute the fault throw via some method of
correlation of seismic events across the fault boundary. This step has
made the bold assumption that we have a priori knowledge of where these
faults are.
Much of this paper has been devoted to documenting the efforts to date
in isolating the position of faults and a means of measuring the
displacement across faults. See Figure 14.
The various tools seem to be available to construct a workflow for
creating the displacement field:
Determine the location of faults
Determine the areas of event continuity
Compute the orientation in continuous areas
Compute fault throw along fault planes
Combine orientation displacement with fault throw displacement to get 3D
displacement
Quality control to correct erroneous estimates will be necessary, but
could potentially be reduced to manual intervention in a sub-set of the
data set, focusing the interpreter’s time and energy on the difficult
regions and let automation help us where appropriate.
Besides the attribute workflows, advances in 3D visualization and 3D
interaction capability are going to commoditize volume or geobody
extraction functionality which will include some combination of fault
extraction, horizon extraction, layer extraction, and confined volume
objects such as salt, carbonate build-ups, channels, fracture zones,
etc. These voxel bodies can be directly realized into our 3D geologic
models to freely share across the seismic to simulation activity. For
those that wish to continue with explicit representations, these can be
derived from the voxel presentation either as surfaces or closed
volumes. The next generation workstations offering fault interpretation
automation will combine interactive signal processing, classification
and automatic extraction of features, powerful 3D editing capabilities,
and advanced tools for property filtering at a system level. But not to
worry, we are confident that the familiar cursor crayon will still be
available for emergencies.
We hope that this paper has yielded some insight into the state of the
art for geoscience interpretation automation in general, and also
highlight the advances that are going to impact our ability to quickly
and accurately interpret fault systems. Our limitation is not the
computer hardware or visualization technology at the moment, but a lack
of logical integration of the necessary interactive tools to
intelligently extract the structural field from the seismic volume.
While the technical pieces are all available, the commercial software
offerings still lag behind. Many advances have been made and the
research continues for both explicit and implicit methods of
representing faulted structures. New algorithms for discontinuity
estimation and subsequent feature identification are constantly arriving
at the patent office and presented at international conferences. Let’s
hope the wait is not long for these marvelous tools to reside on our
workstation desktops.
Abbott, W., 1999, U.S. Patent Number 5,982,707 Method and
Apparatus for Determining Geologic Relationships fFor Intersecting
Faults.
Admasu, F., and Toennies, K., 2004, Automatic method for
correlating horizons across faults in 3D seismic data: IEEE Conference
on Computer Vision and Pattern Recognition, Washington DC, June 2004.
Alberts, P., Warner, M., and Lister, D., 2000, Artificial
neural networks for simultaneous multi horizon tracking across
discontinuities: 70th Annual Meeting SEG, Houston, 2000.
Aurnhammer, M., and Tönnies, K., Image processing
algorithm for matching horizons across faults in seismic data: Computer
Vision Group, Otto-von-Guericke University (http://isgwww.cs.uni-magdeburg.de/bv/pub/pdf/IAMG_Melanie.pdf)
Borgos, H., Skov, T., Randen, T., and Sønneland, L.,
2003, Automated geometry extraction from 3D seismic data, in
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Cheng, Y.C., Fairchild, L.H., Farre, J.A., and May, S.R.,
2003, U.S. Patent Number 6,516,274, Method for Imaging Discontinuities
in Seismic Data Using Dip-Steering.
Dee,S., Freeman, B., Yielding, G., Roberts, A., and
Bretan, P., 2005, Best practice in structural geological analysis: First
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Bahorich, M., and Farmer, S., 1995, 3-D seismic
discontinuity for faults and stratigraphic features: The coherency cube:
The Leading Edge, Vol. 24.10, October 1995.
Bahorich, M., and Farmer, S., U.S. Patent Number
5,563,949, Method of Seismic Signal Processing and Exploration, 1996.
Crawford, M., and Medwedeff, D., 1999, U.S. Patent Number
5,987,388, Automated Extraction Of Fault Surfaces From 3-D Seismic
Prospecting Data.
Goff, D.F., Vincent, L., Deal, K.L., Kowalik, W.S.,
Bombarde, S., Lee, S., Volz, W.R., and Jones, R.C., 2003, U.S. Patent
Application Number 20030112704, Process for Interpreting Faults from a
Fault -Enhanced 3-Dimensional Seismic Attribute Volume.
Hocker, C., and Fehmers, G., 2002, Fast structural
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2003, Fast structural interpretation with Structure-oriented Filtering:
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Hutchinson, Suzi, 1997, FAZP!
1.0 offers automated fault picking (http://www.lgc.com/resources/MJ_97.pdf).
Lees, J.A., “Constructing
faults from seed picks by Voxel Tracking: The Leading Edge, Vol. 18.3,
March 1999.
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Bril, B., and de Groot, P., 2001, Identifying faults and gas chimneys
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